import excel "C:\Users\shane\OneDrive\My Documents\Psychology\Miami\Miami Projects and data\Spring 2022\spring_2022_survey_useable_good_cases_only.xlsx", sheet("spring_2022_survey_useable_good") firstrow

clear
import excel "/Users/caseyklofstad/Dropbox/NSF Conspiracy Theory Surveys/spring 2022 survey/data/spring_2022_survey_useable_good_cases_only.xlsx", sheet("spring_2022_survey_useable_good") firstrow

keep Q2 Q4 Q3_1 Q3_2 Q3_3 Q3_4 Q3_5 Q5 ///
Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 ///
Q17_1 Q17_2 Q17_3 Q17_4 Q17_5 Q17_6 Q17_7 ///
Q18_1 Q18_2 Q19_1 Q19_2 Q20_1 Q20_2 Q20_3 ///
Q21_1 Q21_2 Q21_3 Q22_1 Q22_2 Q22_3 COVCONS_1 ///
COVCONS_2 COVCONS_3 COVCONS_4 Q17_1 Q17_2 ///
Q17_3 Q17_4 Q17_5 Q17_6 Q17_7 Q18_1 Q18_2 Q19_1 ///
Q19_2 Q20_1 Q20_2 Q20_3 Q21_1 Q21_2 Q21_3 Q22_1 ///
Q22_2 Q22_3 Q37_1 Q37_3 Q37_4 Q37_15 Q37_16 Q37_22 ///
Q287_17 Q287_18 Q287_19 Q287_20 Q287_21 Q287_23 ///
Q38_1 Q38_2 Q39_1 Q39_2 Q39_3 Q39_4 Q39_5 Q39_6 ///
Q40_1 Q40_2 CTSCALE_1 CTSCALE_2 CTSCALE_3 CTSCALE_4 ///
Q42_1 Q42_2 Q42_3 Q43_1 Q43_2 Q43_3 Q43_4 Q45_1 Q45_2 ///
Q45_3 Q46_1 Q46_2 Q46_3 Q46_4 Q46_5 Q46_6 MACH_1 MACH_2 ///
MACH_3 MACH_4 NARC_1 NARC_2 NARC_3 NARC_4 ///
PSYC_1 PSYC_2 PSYC_3 PSYC_4 SADISM_1 SADISM_2 ///
SADISM_3 SADISM_4 ANTIEST_1 ANTIEST_2  ANTIEST_3 ANTIEST_4 ///
ANTIEST_5 Q61_1 Q61_2 Q61_3 Q62_1 Q62_2 Q62_3 Q62_4 ///
Q69_1 Q69_2 Q69_3 Q69_4 Q69_5 Q69_6 Q69_7 Q69_8 Q69_9 Q69_10 ///
Q74_1 Q74_2 Q74_3 Q75_1 Q75_2 Q75_3 Q75_4 Q75_5 Q288_1 Q288_2 ///
Q288_3 Q288_4 Q288_5 Q289_1 Q289_2 Q289_3 Q289_4 ///
Q289_5 Q290_1 Q290_2 Q290_3 Q290_4 Q290_5 ///
Q76_1 Q76_2 Q76_3 Q77 Q78 Q79 Q80_1 Q80_2 Q80_3 ///
Q81_1 Q81_2 Q81_3 SCILIT_1 SCILIT_2 SCILIT_3 SCILIT_4 ///
SCILIT_5 SCILIT_6  TRUST_EXP TRUST_ECO TRUST_SCI ///
TRUST_DOC TRUST_LEG TRUST_PRF TRUST_FIN TRUST_PHO ///
TRUST_PHA Q94 Q95_1 Q95_2 Q95_3 Q95_4 Q95_5 /// 
Q96_1 Q96_2 Q96_3 Q96_4 Q96_5 Q96_6 Q96_7 ///
Q96_8 Q96_9 Q96_10 Q96_11 Q96_12 Q104 ///
Q49_1 Q49_2 Q49_3 Q48_1 Q48_2 Q48_3 ///
Q48_4 Q48_5 Q59_1 Q59_2 Q59_3 Q101_1 Q101_2 Q101_3 ///
CONFLICT_1  CONFLICT_2 CONFLICT_3 CONFLICT_4 ///
CONFLICT_5 CONFLICT_6 CONFLICT_7 CONFLICT_8 ///
Q47_1 Q47_2 Q47_3 Q47_4 VICTIM_1 VICTIM_2 VICTIM_3 ///
VICTIM_4 ANOMIE_1 ANOMIE_2 ANOMIE_3 Q64_1 Q64_2 Q64_3 ///
Q73_1 Q73_2 Q73_3 Q73_4 Q73_5 Q83_1 Q83_2 Q83_3 ///
Q83_4 Q84


*Create labels for variables of interest

						*Demographics
clonevar female = Q2
recode female 1=0 2=1
label define female 0 "Male" 1 "Female"
label values female female
label variable female "Female"

clonevar hispanic = Q4
label variable hispanic "Hispanic"
recode hispanic 1=1 2=0
label define hispanic 1 "Yes" 0 "No"
label values hispanic hispanic

clonevar white = Q3_1
mvencode white, mv(0)
label variable white "White"

generate white_hsp = white + hispanic
label variable white_hsp "White, Hispanic or Latino"
recode white_hsp 0=0 1=0 2=1
label define white_hsp 0 "Other" 1 "White, Hispanic"
label values white_hsp white_hsp

generate white_nonhsp = white - white_hsp
label variable white_nonhsp "White, non-Hsp"
label define white_nonhsp 0 "Other" 1 "White, non-Hispanic"
label values white_nonhsp white_nonhsp


clonevar black = Q3_2
mvencode black, mv(0)
label variable black "Black or African American"

generate black_hsp = black + hispanic
label variable black_hsp "Black, Hispanic or Latino"
recode black_hsp 0=0 1=0 2=1
label define black_hsp 0 "Other" 1 "Black, Hispanic"
label values black_hsp black_hsp

generate black_nonhsp = black - black_hsp
label variable black_nonhsp "Black, non-Hsp"
label define black_nonhsp 0 "Other" 1 "Black, non-Hispanic"
label values black_nonhsp black_nonhsp


clonevar asian = Q3_3
mvencode asian, mv(0)
label variable asian "Asian-American or Pacific Islander"

generate asian_hsp = asian + hispanic
label variable asian_hsp "Asian, Hispanic or Latino"
recode asian_hsp 0=0 1=0 2=1
label define asian_hsp 0 "Other" 1 "Asian, Hispanic"
label values asian_hsp asian_hsp

generate asian_nonhsp = asian - asian_hsp
label variable asian_nonhsp "Asian, non-Hsp"
label define asian_nonhsp 0 "Other" 1 "Asian, non-Hispanic"
label values asian_nonhsp asian_nonhsp


clonevar nat_am = Q3_4
mvencode nat_am, mv(0)
label variable nat_am "Native American / American Indian"

generate nat_am_hsp = nat_am + hispanic
label variable nat_am_hsp "Native American, Hispanic or Latino"
recode nat_am_hsp 0=0 1=0 2=1
label define nat_am_hsp 0 "Other" 1 "Native American, Hispanic"
label values nat_am_hsp nat_am_hsp

generate nat_am_nonhsp = nat_am - nat_am_hsp
label variable nat_am_nonhsp "Native American, non-Hsp"
label define nat_am_nonhsp 0 "Other" 1 "Native American, non-Hispanic"
label values nat_am_nonhsp nat_am_nonhsp


clonevar race_other = Q3_5
mvencode race_other, mv(0)
label variable race_other "Race, Other"

clonevar income = Q5
label variable income "Income"
label define income 1 "Less than $25,000" 2 "$25,000 to $49,999" 3 "$50,000 to $74,999" 4 "$75,000 to $99,999" 5 "$100,000 to $149,999" 6 "$150,000 to $199,999" 7 "$200,000 or more"
label values income income

clonevar education = Q6
label variable education "Education"
label define education 1 "Less than high school" 2 "High school graduate or GED" 3 "Some college but no degree (yet)" 4 "2-year college degree" 5 "4-year college degree" 6 "Post-graduate degree (MA, MBA, MD, JD, PhD, etc.)"
label values education education

*Age, from year born (q7)
generate age = 2022 - Q7
summarize age
label variable age "Age"
		*make groupings for demog table
gen age_grp=.
replace age_grp=1 if age<=24
replace age_grp=2 if (age>24 & age<=44)
replace age_grp=3 if (age>44 & age<=64)
replace age_grp=4 if (age>=65)
tab age_grp

clonevar state = Q8
label variable state "State"


					*Political affiliations and ideology
clonevar party_id = Q9
label define party_id 1 "Democrat" 2 "Republican" 3 "Independent" 4 "Something else"
label values party_id party_id
label variable party_id "Party ID"

*Create dummy variables for Party ID
recode party_id 1=1 2/4=0, gen(democrat)
label variable democrat "Democrat"
recode party_id 1=0 2=1 3=0 4=0, gen(republican)
label variable republican "Republican"
recode party_id 1=0 2=0 3=1 4=0, gen(independent)
label variable independent "Independent"
recode party_id 1=0 2=0 3=0 4=1, gen(other_party)
label variable other_party "Other party"

*Create a new recoded variable for Q9 Party_ID (continuous, low to high, with Indeps and Others combined)
recode party_id 1=1 2=3 3=2 4=2, generate(party_id_continuous)
label variable party_id_continuous "Party ID (continuous)"
label define party_id_continuous 1 "Democrat" 2 "Independents/Others" 3 "Republican" 
label values party_id_continuous party_id_continuous


clonevar strong_rep = Q10
recode strong_rep 1=1 2=0
label variable strong_rep "Strong Republican"
label define strong_rep 1 "Strong" 0 "Not very strong"

clonevar strong_dem = Q11
recode strong_dem 1=1 2=0
label variable strong_dem "Strong Democrat"
label define strong_dem 1 "Strong" 0 "Not very strong"

clonevar closer_to = Q12
label variable closer_to "Closer to Republican or Democrat"
label define closer_to 1 "Republican" 2 "Democrat" 3 "Neither"
recode closer_to 1=3 2=1 3=2, generate(closer_to_continuous)
label variable closer_to_continuous "Closer to Rep or Dem (continuous)"
label define closer_to_continuous 1 "Democrat" 2 "Neither" 3 "Republican" 
label values closer_to_continuous closerto_RepDem


clonevar libcon = Q13
label variable libcon "Ideology (Lib-Con)"
label define libcon 1 "Very Liberal" 2 "Liberal" 3 "Slightly Liberal" 4 "Moderate" 5 "Slightly Conservative" 6 "Conservative" 7 "Very Conservative"
label values libcon libcon

*gen 7-point party id (q9-12) to run DEM-REP
gen demrep7=.
replace demrep7=1 if Q11==1
replace demrep7=2 if Q11==2
replace demrep7=3 if Q12==2
replace demrep7=4 if Q12==3
replace demrep7=5 if Q12==1
replace demrep7=6 if Q10==2
replace demrep7=7 if Q10==1
tab demrep7
tab demrep7 Q11
tab demrep7 Q10
tab demrep7 Q12
label variable demrep7 "Party ID (Dem-Rep)"

*fold pid for intensity
gen pid7_str=.
replace pid7_str=4 if demrep7==1
replace pid7_str=3 if demrep7==2
replace pid7_str=2 if demrep7==3
replace pid7_str=1 if demrep7==4
replace pid7_str=2 if demrep7==5
replace pid7_str=3 if demrep7==6
replace pid7_str=4 if demrep7==7
label variable pid7_str "Partisan Strength" 
tab demrep7 pid7_str


*fold ideo for intensity
gen ideo_str=.
replace ideo_str=4 if libcon==1
replace ideo_str=3 if libcon==2
replace ideo_str=2 if libcon==3
replace ideo_str=1 if libcon==4
replace ideo_str=2 if libcon==5
replace ideo_str=3 if libcon==6
replace ideo_str=4 if libcon==7
label variable ideo_str "Ideological Strength"
tab libcon ideo_str


					*Religiosity items
					
*Each items uses a different scale, so must standardize them before combining into same variable
tab Q14
recode Q14 1=4 2=3 3=2 4=1, generate(relig_impt)
label variable relig_impt "Religion importance"


	*Zero "Don't knows", so code DK as 1
tab Q15
recode Q15 1=6 2=5 3=4 4=3 5=2 6=1 7=1, generate(relig_srvs)
label variable relig_srvs "Religious service attendance"


	*Only 36 DKs, so code DK as 1
tab Q16
recode Q16  1=7 2=6 3=5 4=4 5=3 6=2 7=1 8=1, generate(relig_prayer)
label variable relig_prayer "Frequency of prayer"

foreach var of varlist relig_impt relig_srvs relig_prayer{
		qui sum `var'
		replace `var' = ((`var' - `r(min)') / (`r(max)'-`r(min)')) * 10
	}		
	
factor relig_impt relig_srvs relig_prayer, pcf blanks(.3)

alpha relig_impt relig_srvs relig_prayer, generate(religiosity)
label variable religiosity "Religiosity"



					*Conspiracy theory items (continuous)
			
*General conspiracy theory items
recode Q17_1 1=1 2=1 3=0 4=0 5=0, generate(ct_aliens)
label variable ct_aliens "Humans have made contact with aliens and this fact has been deliberately hidden from the public"
label values ct_aliens ct_theory

recode Q17_2 5=1 4=2 3=3 2=4 1=5, generate(ct_jews)
label variable ct_jews "The number of Jews killed by the Nazis during World War II has been exaggerated on purpose."
label values ct_jews ct_theory

recode Q17_3 5=1 4=2 3=3 2=4 1=5, generate(ct_vaccines)
label variable ct_vaccines "The dangers of vaccines are being hidden by the medical establishment."
label values ct_vaccines ct_theory

recode Q17_4 5=1 4=2 3=3 2=4 1=5, generate(ct_GMOs)
label variable ct_GMOs "The dangers of genetically-modified foods are being hidden from the public."
label values ct_GMOs ct_theory

recode Q17_5 5=1 4=2 3=3 2=4 1=5, generate(ct_falseflags)
label variable ct_falseflags "School shootings, like those at Sandy Hook, CT and Parkland, FL are false flag attacks perpetrated by the government."
label values ct_falseflags ct_theory

recode Q17_6 5=1 4=2 3=3 2=4 1=5, generate(ct_nuclear)
label variable ct_nuclear "The true dangers of nuclear power are being covered-up by the government."
label values ct_nuclear ct_theory

recode Q17_7 5=1 4=2 3=3 2=4 1=5, generate(ct_gayagenda)
label variable ct_gayagenda "There is a secret agenda in the public schools to indoctrinate children into gay and trans lifestyles."
label values ct_gayagenda ct_theory


*Partisan conspiracy beliefs
recode Q18_1 5=1 4=2 3=3 2=4 1=5, generate(ct_repub_climate)
label variable ct_repub_climate "Climate change is a hoax perpetrated by corrupt scientists and politicians."
label values ct_repub_climate ct_theory

recode Q18_2 5=1 4=2 3=3 2=4 1=5, generate(ct_Repub_obama)
label variable ct_Repub_obama "Barack Obama faked his citizenship to become president."
label values ct_Repub_obama ct_theory


recode Q19_1 5=1 4=2 3=3 2=4 1=5, generate(ct_Dem_Trump)
label variable ct_Dem_Trump "Donald Trump is a Russian agent."
label values ct_Dem_Trump ct_theory

recode Q19_2 5=1 4=2 3=3 2=4 1=5, generate(ct_Dem_1percent)
label variable ct_Dem_1percent "The 1% of the richest people secretly control the entire American government and economy."
label values ct_Dem_1percent ct_theory


*QAnon items
recode Q20_1 5=1 4=2 3=3 2=4 1=5, generate(qanon_believer)
label values qanon_believer ct_theory


recode Q20_2 5=1 4=2 3=3 2=4 1=5, generate(qanon_deepstate)
label values qanon_deepstate CT_theory


recode Q20_3 5=1 4=2 3=3 2=4 1=5, generate(qanon_trafficking)
label values qanon_trafficking CT_theory


*Voter fraud items
recode Q21_1 5=1 4=2 3=3 2=4 1=5, generate(vf_Bidenfraud)
label values vf_Bidenfraud ct_theory


recode Q21_2 5=1 4=2 3=3 2=4 1=5, generate(vf_rigged)
label values vf_rigged ct_theory


recode Q21_3 5=1 4=2 3=3 2=4 1=5, generate(vf_Repsteal)
label values vf_Repsteal ct_theory


*Russia items
recode Q22_1 5=1 4=2 3=3 2=4 1=5, generate(rus_Putin)
label values rus_Putin ct_theory


recode Q22_2 5=1 4=2 3=3 2=4 1=5, generate(rus_USpolicies)
label values rus_USpolicies ct_theory


recode Q22_3 5=1 4=2 3=3 2=4 1=5, generate(rus_USaid)
label values rus_USaid ct_theory



*COVID-19 items
recode COVCONS_1 5=1 4=2 3=3 2=4 1=5, generate(covid_threat)
label values covid_threat ct_theory


recode COVCONS_2 5=1 4=2 3=3 2=4 1=5, generate(covid_bioweapon)
label values covid_bioweapon ct_theory


recode COVCONS_3 5=1 4=2 3=3 2=4 1=5, generate(covid_vaccine)
label values covid_vaccine ct_theory


recode COVCONS_4 5=1 4=2 3=3 2=4 1=5, generate(covid_China)
label values covid_China ct_theory



					*Conspiracy theory items (dichotomous)
label define ct_theory 0 "Disagree or Not Sure" 1 "Agree"
					
*General conspiracy theory items
recode Q17_1 1=1 2=1 3=0 4=0 5=0, generate(di_aliens)
label variable di_aliens "Humans have made contact with aliens and this fact has been deliberately hidden from the public"
label values di_aliens ct_theory

recode Q17_2 1=1 2=1 3=0 4=0 5=0, generate(di_jews)
label variable di_jews "The number of Jews killed by the Nazis during World War II has been exaggerated on purpose."
label values di_jews ct_theory

recode Q17_3 1=1 2=1 3=0 4=0 5=0, generate(di_vaccines)
label variable di_vaccines "The dangers of vaccines are being hidden by the medical establishment."
label values di_vaccines ct_theory

recode Q17_4 1=1 2=1 3=0 4=0 5=0, generate(di_GMOs)
label variable di_GMOs "The dangers of genetically-modified foods are being hidden from the public."
label values di_GMOs ct_theory

recode Q17_5 1=1 2=1 3=0 4=0 5=0, generate(di_falseflags)
label variable di_falseflags "School shootings, like those at Sandy Hook, CT and Parkland, FL are false flag attacks perpetrated by the government."
label values di_falseflags ct_theory

recode Q17_6 1=1 2=1 3=0 4=0 5=0, generate(di_nuclear)
label variable di_nuclear "The true dangers of nuclear power are being covered-up by the government."
label values di_nuclear ct_theory

recode Q17_7 1=1 2=1 3=0 4=0 5=0, generate(di_gayagenda)
label variable di_gayagenda "There is a secret agenda in the public schools to indoctrinate children into gay and trans lifestyles."
label values di_gayagenda ct_theory


*Partisan conspiracy beliefs
recode Q18_1 1=1 2=1 3=0 4=0 5=0, generate(di_repub_climate)
label variable di_repub_climate "Climate change is a hoax perpetrated by corrupt scientists and politicians."
label values di_repub_climate ct_theory

recode Q18_2 1=1 2=1 3=0 4=0 5=0, generate(di_Repub_obama)
label variable di_Repub_obama "Barack Obama faked his citizenship to become president."
label values di_Repub_obama ct_theory


recode Q19_1 1=1 2=1 3=0 4=0 5=0, generate(di_Dem_Trump)
label variable di_Dem_Trump "Donald Trump is a Russian agent."
label values di_Dem_Trump ct_theory

recode Q19_2 1=1 2=1 3=0 4=0 5=0, generate(di_Dem_1percent)
label variable di_Dem_1percent "The 1% of the richest people secretly control the entire American government and economy."
label values di_Dem_1percent ct_theory


*QAnon items
recode Q20_1 1=1 2=1 3=0 4=0 5=0, generate(di_qanon_believer)
label values di_qanon_believer ct_theory


recode Q20_2 1=1 2=1 3=0 4=0 5=0, generate(di_qanon_deepstate)
label values di_qanon_deepstate ct_theory


recode Q20_3 1=1 2=1 3=0 4=0 5=0, generate(di_qanon_trafficking)
label values di_qanon_trafficking ct_theory


*Voter fraud items
recode Q21_1 1=1 2=1 3=0 4=0 5=0, generate(di_Bidenfraud)
label values di_Bidenfraud ct_theory


recode Q21_2 1=1 2=1 3=0 4=0 5=0, generate(di_rigged)
label values di_rigged ct_theory


recode Q21_3 1=1 2=1 3=0 4=0 5=0, generate(di_Repsteal)
label values di_Repsteal ct_theory


*Russia items
recode Q22_1 1=1 2=1 3=0 4=0 5=0, generate(di_rus_Putin)
label values di_rus_Putin ct_theory


recode Q22_2 1=1 2=1 3=0 4=0 5=0, generate(di_rus_USpolicies)
label values di_rus_USpolicies ct_theory


recode Q22_3 1=1 2=1 3=0 4=0 5=0, generate(di_rus_USaid)
label values di_rus_USaid ct_theory


*COVID-19 items
recode COVCONS_1 1=1 2=1 3=0 4=0 5=0, generate(di_covid_threat)
label values di_covid_threat ct_theory


recode COVCONS_2 1=1 2=1 3=0 4=0 5=0, generate(di_covid_bioweapon)
label values di_covid_bioweapon ct_theory


recode COVCONS_3 1=1 2=1 3=0 4=0 5=0, generate(di_covid_vaccine)
label values di_covid_vaccine ct_theory


recode COVCONS_4 1=1 2=1 3=0 4=0 5=0, generate(di_covid_China)
label values di_covid_China ct_theory


**Total conspiracy theories believed**
generate total_CTs = di_aliens + di_jews + di_vaccines + di_GMOs + di_falseflags + di_nuclear + di_gayagenda + di_repub_climate + di_Repub_obama + di_Dem_Trump + di_Dem_1percent + di_qanon_believer + di_qanon_deepstate + di_qanon_trafficking + di_Bidenfraud + di_rigged + di_Repsteal + di_covid_threat + di_covid_bioweapon + di_covid_vaccine + di_covid_China
label variable total_CTs "Total conspiracy theories believed"


					*Political attitudes and engagement
					
*Feeling thermometers
clonevar demParty = Q37_1
label variable demParty "Democrat Party"
clonevar repParty = Q37_3
label variable repParty "Republican Party"
clonevar trump = Q37_4
label variable trump "Donald Trump"
clonevar biden = Q37_15
label variable biden "Joe Biden"
clonevar sanders = Q37_16
label variable sanders "Bernie Sanders"
clonevar putin = Q37_22
label variable putin "Vladimir Putin"

clonevar qAnon = Q287_17
label variable qAnon "QAnon"
clonevar prdBoys = Q287_18
label variable prdBoys "Proud Boys"
clonevar whtNats = Q287_19
label variable whtNats "White Nationalists"
clonevar antifa = Q287_20
label variable antifa "Antifa"
clonevar progrsvs = Q287_21
label variable progrsvs "Progressives"
clonevar china = Q287_23
label variable china "China"

summarize demParty repParty trump biden sanders putin qAnon prdBoys whtNats antifa progrsvs china


*Political participation measures
recode Q38_1 5=1 4=2 3=3 2=4 1=5, generate(office_run) 
label variable office_run "Might run for office"
recode Q38_2 5=1 4=2 3=3 2=4 1=5, generate(office_qual)
label variable office_qual "Qualified for office"

clonevar polpart_protest = Q39_1
label variable polpart_protest "Political protests"


clonevar polpart_meeting = Q39_2
label variable polpart_meeting "Political meetings"


clonevar polpart_contacted = Q39_3
label variable polpart_contacted "Contacted elected official"


clonevar polpart_volunteer = Q39_4
label variable polpart_volunteer "Election volunteer"


clonevar polpart_civdis = Q39_5
label variable polpart_civdis "Civil disobedience"


clonevar polpart_violence = Q39_6
label variable polpart_violence "Political violence"


*Political interest and efficacy
recode Q40_1 5=1 4=2 3=3 2=4 1=5, generate(pol_influence)
label variable pol_influence "Political efficacy"
recode Q40_2 5=1 4=2 3=3 2=4 1=5, generate(pol_follow)
label variable pol_follow "Follow politics"


					**Psychological variables

*Conspiratorial Thinking Scale
recode CTSCALE_1 1=5 2=4 3=3 4=2 5=1, gen(CTS_1)
recode CTSCALE_2 1=5 2=4 3=3 4=2 5=1, gen(CTS_2)
recode CTSCALE_3 1=5 2=4 3=3 4=2 5=1, gen(CTS_3)
recode CTSCALE_4 1=5 2=4 3=3 4=2 5=1, gen(CTS_4)

factor CTS_1 CTS_2 CTS_3 CTS_4, ipf blanks(.3)

	*Now create an overall Conspiracy thinking variable (alpha = .8657)
alpha CTS_1 CTS_2 CTS_3 CTS_4, generate(consp_think)
label variable consp_think "Conspiratorial thinking style"


*Left-wing Authoritarianism (anti-hierarchical attitudes subscale)
recode Q42_1 1=5 2=4 3=3 4=2 5=1, generate(LWA_1)
recode Q42_2 1=5 2=4 3=3 4=2 5=1, generate(LWA_2)
recode Q42_3 1=5 2=4 3=3 4=2 5=1, generate(LWA_3)

factor LWA_1 LWA_2 LWA_3, ipf blanks(.3)

	*Reliability of LWA (alpha = .8954)
alpha LWA_1 LWA_2 LWA_3, generate(left_wing_auth)
label variable left_wing_auth "Left-wing Authoritarianism"


*Right-wing Authoritarianism (Item 2 already reverse-coded)
recode Q43_1 1=5 2=4 3=3 4=2 5=1, generate(RWA_1)
clonevar RWA_2 = Q43_2
recode Q43_3 1=5 2=4 3=3 4=2 5=1, generate(RWA_3)
recode Q43_4 1=5 2=4 3=3 4=2 5=1, generate(RWA_4)

factor RWA_1 RWA_2 RWA_3 RWA_4, ipf blanks(.3)

	*RWA_2 loads poorly with other items and crossloads on F1 and F2. 	Consider dropping?

*Reliability of RWA (poor reliability, alpha = .6642; dropping RWA_2 increases alpha to .6848)
alpha RWA_1 RWA_3 RWA_4, generate(right_wing_auth)
label variable right_wing_auth "Right-wing Authoritarianism"


*Trust scale (alpha = .7563)
recode Q45_1 1=5 2=4 3=3 4=2 5=1, generate(trust_ppl)
recode Q45_2 1=5 2=4 3=3 4=2 5=1, generate(trust_govt)
recode Q45_3 1=5 2=4 3=3 4=2 5=1, generate(trust_cops)

factor trust_ppl trust_govt trust_cops, ipf blanks(.3)

alpha trust_ppl trust_govt trust_cops, gen(trust_general)
label variable trust_general "Trust"
label variable trust_ppl "Trust in people"
label variable trust_govt "Trust in government"
label variable trust_cops"Trust in police"



*Need for Chaos Scale (alpha = .9176)
recode Q46_1 1=5 2=4 3=3 4=2 5=1, generate(NFC_1)
recode Q46_2 1=5 2=4 3=3 4=2 5=1, generate(NFC_2)
recode Q46_3 1=5 2=4 3=3 4=2 5=1, generate(NFC_3)
recode Q46_4 1=5 2=4 3=3 4=2 5=1, generate(NFC_4)
recode Q46_5 1=5 2=4 3=3 4=2 5=1, generate(NFC_5)
recode Q46_6 1=5 2=4 3=3 4=2 5=1, generate(NFC_6)

factor NFC_1 NFC_2 NFC_3 NFC_4 NFC_5 NFC_6, ipf blanks(.3)

alpha NFC_1 NFC_2 NFC_3 NFC_4 NFC_5 NFC_6, gen(need_chaos)
label variable need_chaos "Need for Chaos"


*Dark Tetrad 

	*Machiavellianism (alpha = .8812)
recode MACH_1 1=5 2=4 3=3 4=2 5=1, generate(Mach1)
recode MACH_2 1=5 2=4 3=3 4=2 5=1, generate(Mach2)
recode MACH_3 1=5 2=4 3=3 4=2 5=1, generate(Mach3)
recode MACH_4 1=5 2=4 3=3 4=2 5=1, generate(Mach4)

factor Mach1 Mach2 Mach3 Mach4, ipf blanks(.3)

alpha Mach1 Mach2 Mach3 Mach4, generate(mach)
label variable mach "Machiavellianism"


	*Narcissism (alpha = .8893)
recode NARC_1 1=5 2=4 3=3 4=2 5=1, generate(Narc1)
recode NARC_2 1=5 2=4 3=3 4=2 5=1, generate(Narc2)
recode NARC_3 1=5 2=4 3=3 4=2 5=1, generate(Narc3)
recode NARC_4 1=5 2=4 3=3 4=2 5=1, generate(Narc4)

factor Narc1 Narc2 Narc3 Narc4, ipf blanks(.3)

alpha Narc1 Narc2 Narc3 Narc4, generate(narc)
label variable narc "Narcissism"


	*Psychoticism (alpha = .8670)
recode PSYC_1 1=5 2=4 3=3 4=2 5=1, generate(Psych1)
recode PSYC_2 1=5 2=4 3=3 4=2 5=1, generate(Psych2)
recode PSYC_3 1=5 2=4 3=3 4=2 5=1, generate(Psych3)
recode PSYC_4 1=5 2=4 3=3 4=2 5=1, generate(Psych4)

factor Psych1 Psych2 Psych3 Psych4, ipf blanks(.3)

alpha Psych1 Psych2 Psych3 Psych4, generate(psycho)
label variable psycho "Psychoticism"


	*Sadism (alpha = .7619)
recode SADISM_1 1=5 2=4 3=3 4=2 5=1, generate(Sadism1)
recode SADISM_2 1=5 2=4 3=3 4=2 5=1, generate(Sadism2)
recode SADISM_3 1=5 2=4 3=3 4=2 5=1, generate(Sadism3)
recode SADISM_4 1=5 2=4 3=3 4=2 5=1, generate(Sadism4)

factor Sadism1 Sadism2 Sadism3 Sadism4, ipf blanks(.3)

alpha Sadism1 Sadism2 Sadism3 Sadism4, generate(sadism)
label variable sadism "Sadism"


	*Dark Tetrad
factor Mach1 Mach2 Mach3 Mach4 Narc1 Narc2 Narc3 Narc4 Psych1 Psych2 Psych3 Psych4 Sadism1 Sadism2 Sadism3 Sadism4, ipf blanks(.3)


alpha Mach1 Mach2 Mach3 Mach4 Narc1 Narc2 Narc3 Narc4 Psych1 Psych2 Psych3 Psych4 Sadism1 Sadism2 Sadism3 Sadism4, generate(dark_tetrad)
label variable dark_tetrad "Dark Tetrad"

	
*Anti-establisment Orientation (AEO, alpha = .7933)
recode ANTIEST_1 1=5 2=4 3=3 4=2 5=1, generate(AEO_1)
recode ANTIEST_2 1=5 2=4 3=3 4=2 5=1, generate(AEO_2)
recode ANTIEST_3 1=5 2=4 3=3 4=2 5=1, generate(AEO_3)
recode ANTIEST_4 1=5 2=4 3=3 4=2 5=1, generate(AEO_4)
recode ANTIEST_5 1=5 2=4 3=3 4=2 5=1, generate(AEO_5)

factor AEO_1 AEO_2 AEO_3 AEO_4 AEO_5, ipf blanks(.3)
rotate, oblimin(0) oblique kaiser blanks(.300)
	*AEO_5 crossloads on two factors

alpha AEO_1 AEO_2 AEO_3 AEO_4 AEO_5, generate(anti_estab)
label variable anti_estab "Anti-Establishment Orientation"


*Dogmatism (alpha = 0.69)
recode Q61_1 1=5 2=4 3=3 4=2 5=1, generate(Dog_1)
recode Q61_2 1=5 2=4 3=3 4=2 5=1, generate(Dog_2)
recode Q61_3 1=5 2=4 3=3 4=2 5=1, generate(Dog_3)

factor Dog_1 Dog_2 Dog_3, ipf blanks(.3)

alpha Dog_1 Dog_2 Dog_3, generate(dogmatism)
label variable dogmatism "Dogmatism"


*Impulsiveness (alpha = 0.74)
recode Q62_1 1=5 2=4 3=3 4=2 5=1, generate(Imp_1)
	*Items 2 thru 4 are already reverse-coded
clonevar Imp_2 = Q62_2
clonevar Imp_3 = Q62_3
clonevar Imp_4 = Q62_4

factor Imp_1 Imp_2 Imp_3 Imp_4, ipf blanks(.3)

alpha Imp_1 Imp_2 Imp_3 Imp_4, generate(impulsive)
label variable impulsive "Impulsiveness"


*Goal endorsement
rename Q69_1 Goals_1
rename Q69_2 Goals_2
rename Q69_3 Goals_3
rename Q69_4 Goals_4
rename Q69_5 Goals_5
rename Q69_6 Goals_6
rename Q69_7 Goals_7
rename Q69_8 Goals_8
rename Q69_9 Goals_9
rename Q69_10 Goals_10


alpha Goals_1 Goals_3 Goals_5 Goals_6 Goals_9, generate(goals_agentic)
label variable goals_agentic "Agentic goals"
**Alpha = .8716

alpha Goals_2 Goals_4 Goals_7 Goals_8 Goals_10, generate(goals_communal)
label variable goals_communal "Communal goals"
**Alpha = .8744

*Paranoia (alpha = .9112)
recode Q74_1 1=5 2=4 3=3 4=2 5=1, generate(Para_1)
recode Q74_2 1=5 2=4 3=3 4=2 5=1, generate(Para_2)
recode Q74_3 1=5 2=4 3=3 4=2 5=1, generate(Para_3)

factor Para_1 Para_2 Para_3, ipf blanks(.3)

alpha Para_1 Para_2 Para_3, generate(paranoia)
label variable paranoia "Paranoia"


*Positive and Negative Affect Schedule - Short Form (PANAS-SF)
clonevar PNAS_1 = Q75_1
clonevar PNAS_2 = Q75_2
clonevar PNAS_3 = Q75_3
clonevar PNAS_4 = Q75_4
clonevar PNAS_5 = Q75_5
clonevar PNAS_6 = Q288_1
clonevar PNAS_7 = Q288_2
clonevar PNAS_8 = Q288_3
clonevar PNAS_9 = Q288_4
clonevar PNAS_10 = Q288_5
clonevar PNAS_11 = Q289_1
clonevar PNAS_12 = Q289_2
clonevar PNAS_13 = Q289_3
clonevar PNAS_14 = Q289_4
clonevar PNAS_15 = Q289_5
clonevar PNAS_16 = Q290_1
clonevar PNAS_17 = Q290_2
clonevar PNAS_18 = Q290_3
clonevar PNAS_19 = Q290_4
clonevar PNAS_20 = Q290_5


generate pos_affect = (PNAS_1 + PNAS_3 + PNAS_5 + PNAS_9 + PNAS_10 + PNAS_12 + PNAS_14 + PNAS_16 + PNAS_17 + PNAS_19)

alpha PNAS_1 PNAS_3 PNAS_5 PNAS_9 PNAS_10 PNAS_12 PNAS_14 PNAS_16 PNAS_17 PNAS_19
label variable pos_affect "Positive Affect"
*POS_AFFECT alpha = .8395

generate neg_affect = (PNAS_2 + PNAS_4 + PNAS_6 + PNAS_7 + PNAS_8 + PNAS_11 + PNAS_13 + PNAS_15 + PNAS_18 + PNAS_20)

alpha PNAS_2 PNAS_4 PNAS_6 PNAS_7 PNAS_8 PNAS_11 PNAS_13 PNAS_15 PNAS_18 PNAS_20
label variable neg_affect "Negative Affect"
*NEG_AFFECT alpha = .8820



*Numbers and patterns (AQ-Short; alpha = 0.8201)

recode Q76_1 1=5 2=4 3=3 4=2 5=1, generate(num_pat1)
recode Q76_2 1=5 2=4 3=3 4=2 5=1, generate(num_pat2)
recode Q76_3 1=5 2=4 3=3 4=2 5=1, generate(num_pat3)

factor num_pat1 num_pat2 num_pat3, ipf blanks(.3)

alpha num_pat1 num_pat2 num_pat3, generate(patternicity)
label variable patternicity "Patternicity (self-reported)"


*Subjective numeracy (alpha = 0.8086)
rename Q77 subnum1
rename Q78 subnum2
rename Q79 subnum3

factor subnum1 subnum2 subnum3, ipf blanks(.3)

alpha subnum1 subnum2 subnum3, generate(subj_num)
label variable subj_num "Subjective Numeracy"


*Simple solutions (alpha = 0.8030)
recode Q80_1 1=5 2=4 3=3 4=2 5=1, generate(simpsol1)
recode Q80_2 1=5 2=4 3=3 4=2 5=1, generate(simpsol2)
recode Q80_3 1=5 2=4 3=3 4=2 5=1, generate(simpsol3)

factor simpsol1 simpsol2 simpsol3, ipf blanks(.3)

alpha simpsol1 simpsol2 simpsol3, generate(simple_solutions)
label variable simple_solutions "Desire for simple solutions"


*Intolerance of Uncertainty (alpha = 0.7920)
recode Q81_1 1=5 2=4 3=3 4=2 5=1, generate(uncertain1)
recode Q81_2 1=5 2=4 3=3 4=2 5=1, generate(uncertain2)
recode Q81_3 1=5 2=4 3=3 4=2 5=1, generate(uncertain3)

factor uncertain1 uncertain2 uncertain3, ipf blanks(.3)

alpha uncertain1 uncertain2 uncertain3, generate(intol_uncertainty)
label variable intol_uncertainty "Intolerance of Uncertainty"


*Science literacy
recode SCILIT_1 1=1 2=0, generate(scilit_1)
recode SCILIT_2 1=1 2=0, generate(scilit_2)
recode SCILIT_3 1=1 2=0, generate(scilit_3)
recode SCILIT_4 1=1 2=0, generate(scilit_4)
recode SCILIT_5 1=1 2=0, generate(scilit_5)
recode SCILIT_6 1=1 2=0, generate(scilit_6)

factor scilit_1 scilit_2 scilit_3 scilit_4 scilit_5 scilit_6, pcf blanks(.3)

generate science_literacy = scilit_1 + scilit_2 + scilit_3 + scilit_4 + scilit_5 + scilit_6
label variable science_literacy "Scientific Literacy"


*Anti-intellectualism (alpha = 0.9292; Items are reverse-coded already, so higher scores = less trust in experts = higher anti-intellectualism)
factor TRUST_EXP TRUST_ECO TRUST_SCI TRUST_DOC TRUST_LEG TRUST_PRF TRUST_FIN TRUST_PHO TRUST_PHA, blanks (.3)

alpha TRUST_EXP TRUST_ECO TRUST_SCI TRUST_DOC TRUST_LEG TRUST_PRF TRUST_FIN TRUST_PHO TRUST_PHA, generate(anti_intellectual)
label variable anti_intellectual "Anti-intellectualism"


*Spread Misinformation
recode Q94 1=5 2=4 3=3 4=2 5=1, generate(spreads_misinfo)
label variable spreads_misinfo "Shares false information"
label define spreads_misinfo 1 "SD" 2 "D" 3 "N" 4 "A" 5 "SA"
label values spreads_misinfo spreads_misinfo

recode Q94 1=1 2=1 3=0 4=0 5=0, generate(spreads_YN)
label variable spreads_YN "Spreads misinformation Y/N"
label define spreads_YN 0 "No" 1 "Yes"
label values spreads_YN spreads_YN


*Offline Media Use
recode Q95_1 1=5 2=4 3=3 4=2 5=1, generate(OFFLINE_network)
recode Q95_2 1=5 2=4 3=3 4=2 5=1, generate(OFFLINE_cable)
recode Q95_3 1=5 2=4 3=3 4=2 5=1, generate(OFFLINE_local)
recode Q95_4 1=5 2=4 3=3 4=2 5=1, generate(OFFLINE_print)
recode Q95_5 1=5 2=4 3=3 4=2 5=1, generate(OFFLINE_radio)

label variable OFFLINE_network "Network TV News"
label variable OFFLINE_cable "Cable TV News"
label variable OFFLINE_local "Local TV News"
label variable OFFLINE_print "Print newspapers"
label variable OFFLINE_radio "Radio"


factor OFFLINE_network OFFLINE_cable OFFLINE_local OFFLINE_print OFFLINE_radio, pcf blanks(.3)

rotate, oblimin(0) oblique kaiser blanks(.300)

*PCA supports a 1-factor solution, so create a mean score for Legacy Media Use
alpha OFFLINE_network OFFLINE_cable OFFLINE_local OFFLINE_print OFFLINE_radio, generate(legacy_media)
label variable legacy_media "Legacy News Media Use"


*Online Media Use
recode Q96_1 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_newspapers)
recode Q96_2 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_newsmags)
recode Q96_3 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_blogs)
recode Q96_4 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_YouTube)
recode Q96_5 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_Facebook)
recode Q96_6 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_Twitter)
recode Q96_7 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_Reddit)
recode Q96_8 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_Instagram)
recode Q96_9 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_TVnewssites)
recode Q96_10 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_8kun)
recode Q96_11 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_Telegram)
recode Q96_12 1=5 2=4 3=3 4=2 5=1, generate(ONLINE_TruthSocial)

label variable ONLINE_newspapers "Online newspapers"
label variable ONLINE_newsmags "Online news magazines"
label variable ONLINE_blogs "Blogs"
label variable ONLINE_YouTube "YouTube"
label variable ONLINE_Facebook "Facebook"
label variable ONLINE_Twitter "Twitter"
label variable ONLINE_Reddit "Reddit"
label variable ONLINE_Instagram "Instagram"
label variable ONLINE_TVnewssites "TV News websites"
label variable ONLINE_8kun "8Kun"
label variable ONLINE_Telegram "Telegram"
label variable ONLINE_TruthSocial "Truth Social"


factor ONLINE_newspapers ONLINE_newsmags ONLINE_blogs ONLINE_YouTube ONLINE_Facebook ONLINE_Twitter ONLINE_Reddit ONLINE_Instagram ONLINE_TVnewssites ONLINE_8kun ONLINE_Telegram ONLINE_TruthSocial, pcf blanks(.3)

rotate, oblimin(0) oblique kaiser blanks(.300)

	*PCA supports a 3-factor solution (PCA1 = alt websites; PCA2 = popular social media sites; PCA3 = mainstream media sites). Can be treated as 1 variable or as 3.

	*As 1 variable
alpha ONLINE_newspapers ONLINE_newsmags ONLINE_blogs ONLINE_YouTube ONLINE_Facebook ONLINE_Twitter ONLINE_Reddit ONLINE_Instagram ONLINE_TVnewssites ONLINE_8kun ONLINE_Telegram ONLINE_TruthSocial, generate(online_media_ALL)

	*As 3 variables
alpha ONLINE_blogs ONLINE_Reddit ONLINE_8kun ONLINE_Telegram ONLINE_TruthSocial, generate(online_ALTmedia)
label variable online_ALTmedia "Alternative Social Media"

alpha ONLINE_YouTube ONLINE_Facebook ONLINE_Twitter ONLINE_Instagram, generate(online_SOCIALmedia)
label variable online_SOCIALmedia "Mainstream Social Media"

alpha ONLINE_newspapers ONLINE_newsmags ONLINE_TVnewssites, generate(online_MSmedia)
label variable online_MSmedia "Online Mainstream News Media"


**Cryptocurrency (Q104 Do you currently, or have you ever, owned crypto-currency?)**
recode Q104 1=1 2=0, generate(crypto)
label define crypto 0 "No" 1 "Yes"
label values crypto crypto
label variable crypto "Own/Owned cryptocurrency"


*Predisposition to argue (alpha = .7230)
recode Q49_1 1=5 2=4 3=3 4=2 5=1, generate(Argue_1)
recode Q49_2 1=5 2=4 3=3 4=2 5=1, generate(Argue_2)
recode Q49_3 1=5 2=4 3=3 4=2 5=1, generate(Argue_3)

factor Argue_1 Argue_1 Argue_3, ipf blanks(.3)

alpha Argue_1 Argue_2 Argue_3, generate(argue)
label variable argue "Argumentativeness"


*Christian Nationalism scale (item 3 already reverse-coded)
recode Q48_1 1=5 2=4 3=3 4=2 5=1, generate(chrsNat_1)
recode Q48_2 1=5 2=4 3=3 4=2 5=1, generate(chrsNat_2)
clonevar chrsNat_3 = Q48_3
recode Q48_4 1=5 2=4 3=3 4=2 5=1, generate(chrsNat_4)
recode Q48_5 1=5 2=4 3=3 4=2 5=1, generate(chrsNat_5)

factor chrsNat_1 chrsNat_2 chrsNat_3 chrsNat_4 chrsNat_5, ipf blanks(.3)

	*ChrsNat_3 loads very poorly with other items. Best to drop it. Alpha of full scale = 0.8195. Alpha dropping item 3 = 0.8900

generate christ_nat = chrsNat_1 + chrsNat_2 + chrsNat_4 + chrsNat_5
label variable christ_nat "Christian Nationalism"

alpha chrsNat_1 chrsNat_2 chrsNat_3 chrsNat_4 chrsNat_5 
alpha chrsNat_1 chrsNat_2 chrsNat_4 chrsNat_5

*National Narcissism (alpha = 0.8006)
recode Q59_1 1=5 2=4 3=3 4=2 5=1, generate(natNarc_1)
recode Q59_2 1=5 2=4 3=3 4=2 5=1, generate(natNarc_2)
recode Q59_3 1=5 2=4 3=3 4=2 5=1, generate(natNarc_3)

factor natNarc_1 natNarc_2 natNarc_3, ipf blanks(.3)

alpha natNarc_1 natNarc_2 natNarc_3, generate(national_narc)
label variable national_narc "National Narcissism"


*Gendered Nationalism (alpha = 0.8590)
recode Q101_1 1=5 2=4 3=3 4=2 5=1, generate(genNat_1)
recode Q101_2 1=5 2=4 3=3 4=2 5=1, generate(genNat_2)
recode Q101_3 1=5 2=4 3=3 4=2 5=1, generate(genNat_3)

factor genNat_1 genNat_2 genNat_3, ipf blanks(.3)

alpha genNat_1 genNat_2 genNat_3, generate(gendered_nat)
label variable gendered_nat "Gendered Nationalism"


*General Conflict Tactic Scale (GCTS, alpha = .7214)
recode CONFLICT_1 1=0 2=1, generate(GCTS_1)
recode CONFLICT_2 1=0 2=1, generate(GCTS_2)
recode CONFLICT_3 2=0 1=1, generate(GCTS_3)
recode CONFLICT_4 2=0 1=1, generate(GCTS_4)
recode CONFLICT_5 2=0 1=1, generate(GCTS_5)
recode CONFLICT_6 2=0 1=1, generate(GCTS_6)
recode CONFLICT_7 2=0 1=1, generate(GCTS_7)
recode CONFLICT_8 2=0 1=1, generate(GCTS_8)

generate conflict = GCTS_3 + GCTS_4 + GCTS_5 + GCTS_6 + GCTS_7 + GCTS_8
label variable conflict "Conflict"

factor GCTS_1 GCTS_2 GCTS_3 GCTS_4 GCTS_5 GCTS_6 GCTS_7 GCTS_8, pcf blanks(.3)

	*GCTS_1 and GCTS_2 load on a separate, weak factor. GCTS_3 cross loads. Consider dropping all three? Increases reliability to alpha = .8544

alpha GCTS_1 GCTS_2 GCTS_3 GCTS_4 GCTS_5 GCTS_6 GCTS_7 GCTS_8
alpha GCTS_3 GCTS_4 GCTS_5 GCTS_6 GCTS_7 GCTS_8


*Hong Reactance Scale (alpha = .7626)
recode Q47_1 1=5 2=4 3=3 4=2 5=1, generate(React_1)
recode Q47_2 1=5 2=4 3=3 4=2 5=1, generate(React_2)
recode Q47_3 1=5 2=4 3=3 4=2 5=1, generate(React_3)
recode Q47_4 1=5 2=4 3=3 4=2 5=1, generate(React_4)

factor React_1 React_2 React_3 React_4, ipf blanks(.3)

alpha React_1 React_2 React_3 React_4, generate(reactance)
label variable reactance "Reactance"


*Victimhood Scale (alpha = .8967)
recode VICTIM_1 1=5 2=4 3=3 4=2 5=1, generate(victim1)
recode VICTIM_2 1=5 2=4 3=3 4=2 5=1, generate(victim2)
recode VICTIM_3 1=5 2=4 3=3 4=2 5=1, generate(victim3)
recode VICTIM_4 1=5 2=4 3=3 4=2 5=1, generate(victim4)

factor victim1 victim2 victim3 victim4, ipf blanks(.3)

alpha victim1 victim2 victim3 victim4, generate(victimhood)
label variable victimhood "Victimhood"


*Anomie (reliability = 0.4440. Terrible alpha, consider not using scale)
recode ANOMIE_1 1=5 2=4 3=3 4=2 5=1
recode ANOMIE_2 1=5 2=4 3=3 4=2 5=1
*ANOMIE_3 is already reverse-coded

alpha ANOMIE_1 ANOMIE_2 ANOMIE_3, generate (anomie)
label variable anomie "Anomie"



*Nostalgia (alpha = 0.5096. Terrible alpha, consider not using scale)
recode Q64_1 1=5 2=4 3=3 4=2 5=1, generate(nostal_1)
recode Q64_3 1=5 2=4 3=3 4=2 5=1, generate(nostal_3)
	*Item 2 is already reverse-coded
clonevar nostal_2 = Q64_2

factor nostal_1 nostal_2 nostal_3, ipf blanks(.3)

alpha nostal_1 nostal_2 nostal_3, generate(nostalgia)
label variable nostalgia "Nostalgia"


*Schizotypal Personality Questionnaire-Brief (SPQ-B); Cognitive Perceptual Dimension (alpha = 0.8720)
recode Q73_1 1=5 2=4 3=3 4=2 5=1, generate(SPQ_1)
recode Q73_2 1=5 2=4 3=3 4=2 5=1, generate(SPQ_2)
recode Q73_3 1=5 2=4 3=3 4=2 5=1, generate(SPQ_3)
recode Q73_4 1=5 2=4 3=3 4=2 5=1, generate(SPQ_4)
recode Q73_5 1=5 2=4 3=3 4=2 5=1, generate(SPQ_5)

factor SPQ_1 SPQ_2 SPQ_3 SPQ_4 SPQ_5, ipf blanks(.3)

alpha SPQ_1 SPQ_2 SPQ_3 SPQ_4 SPQ_5, generate(schizotypal)
label variable schizotypal "Schizotypal Personality - Cog Perceptual"


*Denialism (alpha = 0.8624)
recode Q83_1 1=5 2=4 3=3 4=2 5=1, generate(denial1)
recode Q83_2 1=5 2=4 3=3 4=2 5=1, generate(denial2)
recode Q83_3 1=5 2=4 3=3 4=2 5=1, generate(denial3)
recode Q83_4 1=5 2=4 3=3 4=2 5=1, generate(denial4)


factor denial1 denial2 denial3 denial4, ipf blanks(.3)

alpha denial1 denial2 denial3 denial4, generate(denialism)
label variable denialism "Denialism"


*Confidence in the Scientific Community
recode Q84 1=5 2=4 3=3 4=2 5=1, generate(scicom_confidence)
label variable scicom_confidence "Confidence in Scientific Community"


							***ANALYSES***


*Generating frequency tables for main demographics variables
tab female
tab age_grp
tab income
tab education
tab party_id
tab crypto


*Means and SDs for key study variables
summarize crypto female age income education white black hispanic asian nat_am race_other

summarize demrep7 left_wing_auth right_wing_auth religiosity democrat republican independent other_party

summarize consp_think spreads_misinfo need_chaos anti_estab narc mach psycho sadism 

summarize patternicity simple_solutions subj_num anti_intellectual science_literacy

summarize dogmatism paranoia pos_affect neg_affect intol_uncertainty 


							**Figure #1**
*Bar graph of responses to cryptocurrency question*
ssc install cleanplots
set scheme cleanplots

graph bar (percent), over(crypto, label(labsize(medlarge))) bargap(10) bar(1, fcolor(dknavy) fintensity(80) lcolor(black) lwidth(medium) lpattern(solid) lalign(outside)) bar(2, lcolor(black) lwidth(medium) lpattern(solid) lalign(outside)) ytitle("Percent of respondents", size(medium)) title("Do you currently, or have you ever, owned cryptocurrency?", size(large))

ttest age, by(crypto) welch

							**Figure #2
*Point biserial correlations between crypto and demographics variables


ci2 crypto female, corr
ci2 crypto age, corr
ci2 crypto income, corr
ci2 crypto education, corr
ci2 crypto religiosity, corr
ci2 crypto white, corr
ci2 crypto black, corr
ci2 crypto hispanic, corr
ci2 crypto asian, corr
ci2 crypto nat_am, corr
ci2 crypto race_other, corr


matrix drop _all

		*make a 1-row matrix w/ correlations
matrix A = (-0.244,-0.358,0.202,0.203,0.200,-0.041,0.066,0.073)
		*make a 1-row matrix w/ lower CIs
matrix B = (-0.284,-0.395,0.159,0.161,0.157,-0.085,0.022,0.029)
		*make a 1-row matrix w/ upper CIs
matrix C = (-0.202,-0.319,0.243,0.245,0.242,0.003,0.109,0.116)
		*combine the lower and upper CIs, where lower is r1 and upper is r2
matrix D = (B \ C)
		*make the plot
		
set scheme s2color

ssc install coefplot

coefplot matrix(A), ci(D) xline(0, lcolor(black) lwidth(thin) lpattern(dash)) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medlarge)) graphregion(fcolor(white)) ylabel(,labsize(medsmall)) xlabel(, nogrid labsize(small)) legend(off) coeflabels(c1 = "Female" c2 = "Age" c3 = "Income" c4 = "Education" c5 = "Religiosity" c6 = "White" c7 = "Black" c8 = "Hispanic")


asdoc pwcorr crypto female age income education religiosity white black hispanic, sig star(.05)

								**Figure #3**
								
**Point biserial correlations between crypto and left-right political orientations

**Horizontal bar graph of correlations with feeling thermometers
ssc install ci2
ci2 crypto demParty, corr
ci2 crypto repParty, corr
ci2 crypto biden, corr
ci2 crypto sanders, corr
ci2 crypto trump, corr
ci2 crypto progrsvs, corr
ci2 crypto demrep7, corr
ci2 crypto libcon, corr
 

matrix drop _all

		*make a 1-row matrix w/ correlations
matrix A = (0.172,0.088,0.173,0.147,0.109,0.234,-0.166,-0.095)
		*make a 1-row matrix w/ lower CIs
matrix B = (0.129,0.044,0.130,0.103,0.065,0.191,-0.209,-0.138)
		*make a 1-row matrix w/ upper CIs
matrix C = (0.215,0.131,0.215,0.190,0.152,0.276,-0.123,-0.051)
		*combine the lower and upper CIs, where lower is r1 and upper is r2
matrix D = (B \ C)
		*make the plot
		
set scheme s2color

coefplot matrix(A), ci(D) xline(0, lcolor(black) lwidth(thin) lpattern(dash)) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medlarge)) graphregion(fcolor(white)) ylabel(,labsize(medsmall)) xlabel(, nogrid labsize(small)) legend(off) coeflabels(c1 = "Democrat Party" c2 = "Republican Party" c3 = "Joe Biden" c4 = "Bernie Sanders" c5 = "Donald Trump" c6 = "Progressives" c7 = "Partisanship (Dem - Rep)" c8 = "Ideology (Very lib - Very con)")

ssc install asdoc
asdoc pwcorr crypto demParty repParty biden sanders trump progrsvs demrep7 libcon, sig star(.05)


								**Figure #4**
								
**Point biserial correlations between crypto and political extremity

**Horizontal bar graph of correlations with feeling thermometers

ci2 crypto pid7_str, corr
ci2 crypto ideo_str, corr
ci2 crypto putin, corr
ci2 crypto qAnon, corr
ci2 crypto prdBoys, corr
ci2 crypto whtNats, corr
ci2 crypto antifa, corr
ci2 crypto left_wing_auth, corr
ci2 crypto right_wing_auth, corr

matrix drop _all
		*make a 1-row matrix w/ correlations
matrix A = (0.156,0.148,0.340,0.347,0.325,0.350,0.278,0.259,0.091)
		*make a 1-row matrix w/ lower CIs
matrix B = (0.113,0.104,0.300,0.307,0.284,0.310,0.236,0.217,0.047)
		*make a 1-row matrix w/ upper CIs
matrix C = (0.199,0.190,0.378,0.386,0.365,0.389,0.319,0.299,0.134)
		*combine the lower and upper CIs, where lower is r1 and upper is r2
matrix D = (B \ C)
		*make the plot
		
set scheme s2color

coefplot matrix(A), ci(D) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medlarge)) graphregion(fcolor(white)) ylabel(,labsize(medsmall)) xlabel(, nogrid labsize(small)) legend(off) coeflabels(c1 = "Partisan intensity" c2 = "Ideological intensity" c3 = "Vladimir Putin" c4 = "QAnon" c5 = "Proud Boys" c6 = "White Nationalists" c7 = "Antifa" c8 = "Left-wing Authoritarianism" c9 = "Right-wing Authoritarianism") 


asdoc pwcorr crypto pid7_str ideo_str putin qAnon prdBoys whtNats antifa left_wing_auth right_wing_auth, sig star(.05)



								**Figure #5**
								
**Point biserial correlations between crypto and non-Left/Right Political Orientations 

**Horizontal bar graph of correlations with feeling thermometers

ci2 crypto pol_follow, corr
ci2 crypto pol_influence, corr
ci2 crypto anti_estab, corr
ci2 crypto trust_ppl, corr
ci2 crypto trust_govt, corr
ci2 crypto trust_cops, corr
ci2 crypto denialism, corr
ci2 crypto christ_nat, corr
ci2 crypto national_narc, corr
ci2 crypto gendered_nat, corr


matrix drop _all
		*make a 1-row matrix w/ correlations
matrix A = (0.173,0.213,0.106,0.104,0.178,0.034,0.102,0.155,0.200,0.211)
		*make a 1-row matrix w/ lower CIs
matrix B = (0.130,0.171,0.062,0.060,0.135,-0.009,0.058,0.112,0.158,0.168)
		*make a 1-row matrix w/ upper CIs
matrix C = (0.215,0.254,0.149,0.147,0.220,0.078,0.145,0.198,0.242,0.252)
		*combine the lower and upper CIs, where lower is r1 and upper is r2
matrix D = (B \ C)
		*make the plot
		
set scheme s2color

coefplot matrix(A), ci(D) xline(0, lcolor(black) lwidth(thin) lpattern(dash)) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medlarge)) graphregion(fcolor(white)) ylabel(,labsize(medsmall)) xlabel(0 .1 .2 .3 .4, nogrid labsize(small)) legend(off) coeflabels(c1 = "Political interest" c2 = "Political efficacy" c3 = "Populism" c4 = "Trust in people" c5 = "Trust in government" c6 = "Trust in police" c7 = "Denialism" c8 = "Christian Nationalism" c9 = "National Narcissism" c10 = "Gendered Nationalism") 


asdoc pwcorr crypto pol_follow pol_influence anti_estab trust_ppl trust_govt trust_cops denialism christ_nat national_narc gendered_nat, sig star(.05)


								**Figure #6**
*Interpersonal and political behaviors
*Horizontal bar graph


ci2 crypto polpart_protest, corr
ci2 crypto polpart_meeting, corr
ci2 crypto polpart_contacted, corr
ci2 crypto polpart_volunteer, corr
ci2 crypto polpart_civdis, corr
ci2 crypto polpart_violence, corr
ci2 crypto argue, corr
ci2 crypto conflict, corr
ci2 crypto office_qual, corr
ci2 crypto office_run, corr


matrix drop _all

		*make a 1-row matrix w/ correlations
matrix A = (0.342,0.371,0.225,0.373,0.326,0.332,0.383,0.269,0.333,0.416)
		*make a 1-row matrix w/ lower CIs
matrix B = (0.303,0.333,0.183,0.334,0.286,0.292,0.344,0.228,0.293,0.379)
		*make a 1-row matrix w/ upper CIs
matrix C = (0.381,0.409,0.266,0.410,0.365,0.370,0.419,0.310,0.371,0.451)
		*combine the lower and upper CIs, where lower is r1 and upper is r2
matrix D = (B \ C)

		*make the plot
		
set scheme s2color

coefplot matrix(A), ci(D) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medlarge)) graphregion(fcolor(white)) ylabel(,labsize(medsmall)) xlabel(0 .1 .2 .3 .4 .5, nogrid labsize(small)) legend(off) coeflabels(c1 = "Political protests" c2 = "Political meetings" c3 = "Contacted elected official" c4 = "Election volunteer" c5 = "Civil disobedience" c6 = "Political violence" c7 = "Argumentativeness" c8 = "Conflict in disagreements" c9 = "Qualified for office" c10 = "Might run for office") 


asdoc pwcorr crypto polpart_protest polpart_meeting polpart_contacted polpart_volunteer polpart_civdis polpart_violence argue conflict office_qual office_run, sig star(.05)


								**Figure #7**
								
**Point biserial correlations between crypto and personality, emotional, and motivational characteristics 

**Horizontal bar graph of correlations with feeling thermometers

ci2 crypto need_chaos, corr
ci2 crypto reactance, corr
ci2 crypto narc, corr
ci2 crypto psycho, corr
ci2 crypto mach, corr
ci2 crypto sadism, corr
ci2 crypto victimhood, corr
ci2 crypto dogmatism, corr
ci2 crypto impulsive, corr
ci2 crypto schizotypal, corr
ci2 crypto paranoia, corr
ci2 crypto goals_agentic, corr
ci2 crypto goals_communal, corr
ci2 crypto pos_affect, corr
ci2 crypto neg_affect, corr


matrix drop _all
		*make a 1-row matrix w/ correlations
matrix A = (0.350,0.111,0.382,0.271,0.288,0.273,0.176,0.232,-0.083,0.354,0.351,0.312,0.080,0.163,0.247)
		*make a 1-row matrix w/ lower CIs
matrix B = (0.311,0.068,0.344,0.229,0.247,0.232,0.133,0.190,-0.126,0.315,0.312,0.272,0.036,0.120,0.205)
		*make a 1-row matrix w/ upper CIs
matrix C = (0.388,0.154,0.419,0.311,0.327,0.313,0.218,0.273,-0.039,0.391,0.389,0.351,0.123,0.205,0.287)
		*combine the lower and upper CIs, whreby lower is r1 and upper is r2
matrix D = (B \ C)
		*make the plot
		
set scheme s2color

coefplot matrix(A), ci(D) xline(0, lcolor(black) lwidth(thin) lpattern(dash)) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medlarge)) graphregion(fcolor(white)) ylabel(,labsize(medsmall)) xlabel(, nogrid labsize(small)) legend(off) coeflabels(c1 = "Need for Chaos" c2 = "Psychological reactance" c3 = "Narcissism" c4 = "Psychopathy" c5 = "Machiavellianism" c6 = "Sadism" c7 = "Victimhood mentality" c8 = "Dogmatism" c9 = "Impulsiveness" c10 = "Schizotypal personality" c11 = "Paranoia" c12 = "Agentic goal motivation" c13 = "Communal goal motivation" c14 = "Positive affect" c15 = "Negative affect") 


asdoc pwcorr crypto need_chaos reactance narc psycho mach sadism victimhood dogmatism impulsive schizotypal paranoia goals_agentic goals_communal pos_affect neg_affect, sig star(.05)


								**Figure #8**
								
**Point biserial correlations between crypto and thinking styles

**Horizontal bar graph of correlations with feeling thermometers

ci2 crypto patternicity, corr
ci2 crypto subj_num, corr
ci2 crypto simple_solutions, corr
ci2 crypto intol_uncertainty, corr
ci2 crypto science_literacy, corr
ci2 crypto consp_think, corr
ci2 crypto total_CTs, corr
ci2 crypto anti_intellectual, corr
ci2 crypto scicom_confidence, corr


matrix drop _all
		*make a 1-row matrix w/ correlations
matrix A = (0.303,0.181,0.274,0.177,0.122,0.229,0.325,-0.162,0.150)
		*make a 1-row matrix w/ lower CIs
matrix B = (0.262,0.138,0.233,0.134,0.078,0.187,0.285,-0.204,0.107)
		*make a 1-row matrix w/ upper CIs
matrix C = (0.342,0.223,0.314,0.219,0.165,0.270,0.364,-0.119,0.193)
		*combine the lower and upper CIs, whreby lower is r1 and upper is r2
matrix D = (B \ C)
		*make the plot
		
set scheme s2color

coefplot matrix(A), ci(D) xline(0, lcolor(black) lwidth(thin) lpattern(dash)) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medlarge)) graphregion(fcolor(white)) ylabel(,labsize(medsmall)) xlabel(-.2 -.1 0 .1 .2 .3 .4, nogrid labsize(small)) legend(off) coeflabels(c1 = "Patternicity" c2 = "Subjective numeracy" c3 = "Desire for simple solutions" c4 = "Intolerance of uncertainty" c5 = "Scientific literacy" c6 = "Conspiracy thinking" c7 = "Total conspiracy theories believed" c8 = "Anti-intellectualism" c9 = "Confidence in Scientific Community") 


asdoc pwcorr crypto patternicity subj_num simple_solutions intol_uncertainty science_literacy consp_think total_CTs anti_intellectual scicom_confidence, sig star(.05)



								**Figure #9**
*Plot of correlations between crypto and media categories

ci2 crypto legacy_media, corr 
ci2 crypto online_ALTmedia, corr 
ci2 crypto online_SOCIALmedia, corr 
ci2 crypto online_MSmedia, corr 


matrix drop _all
		*make a 1-row matrix w/ correlations
matrix A = (0.234,0.540,0.427,0.341)
		*make a 1-row matrix w/ lower CIs
matrix B = (0.192,0.508,0.391,0.302)
		*make a 1-row matrix w/ upper CIs
matrix C = (0.275,0.570,0.463,0.380)
		*combine the lower and upper CIs, whreby lower is r1 and upper is r2
matrix D = (B \ C)
		*make the plot
		
set scheme s2color

coefplot matrix(A), ci(D) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medlarge)) graphregion(fcolor(white)) ylabel(,labsize(medsmall)) xlabel(0 .1 .2 .3 .4 .5 .6, nogrid labsize(small)) legend(off) coeflabels(c1 = "Legacy Mainstream News Media" c2 = "Alternative Social Media" c3 = "Mainstream Social Media" c4 = "Online Mainstream News Media") 

asdoc pwcorr crypto legacy_media online_MSmedia online_SOCIALmedia online_ALTmedia, sig star(.05)



								**Figure #10**
*Forest plot of correlations with individual media sources

**Forest plot of bivariate correlations**

ci2 crypto OFFLINE_network, corr
ci2 crypto OFFLINE_cable, corr
ci2 crypto OFFLINE_local, corr
ci2 crypto OFFLINE_print, corr
ci2 crypto OFFLINE_radio, corr
ci2 crypto ONLINE_newspapers, corr
ci2 crypto ONLINE_newsmags, corr
ci2 crypto ONLINE_blogs, corr
ci2 crypto ONLINE_YouTube, corr
ci2 crypto ONLINE_Facebook, corr
ci2 crypto ONLINE_Twitter, corr
ci2 crypto ONLINE_Reddit, corr
ci2 crypto ONLINE_Instagram, corr
ci2 crypto ONLINE_TVnewssites, corr
ci2 crypto ONLINE_8kun, corr
ci2 crypto ONLINE_Telegram, corr
ci2 crypto ONLINE_TruthSocial, corr


matrix drop _all
		*make a 1-row matrix w/ correlations
matrix A = (0.144,0.182,0.082,0.234,0.214,0.276,0.368,0.447,0.344,0.191,0.446,0.469,0.370,0.200,0.388,0.482,0.401)
		*make a 1-row matrix w/ lower CIs
matrix B = (0.101,0.139,0.038,0.192,0.172,0.235,0.330,0.411,0.305,0.149,0.410,0.435,0.332,0.158,0.350,0.447,0.363)
		*make a 1-row matrix w/ upper CIs
matrix C = (0.187,0.224,0.125,0.275,0.256,0.316,0.406,0.482,0.382,0.233,0.480,0.503,0.407,0.242,0.424,0.515,0.437)
		*combine the lower and upper CIs, whreby lower is r1 and upper is r2
matrix D = (B \ C)
		*make the plot
		
set scheme s2color

coefplot matrix(A), ci(D) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medlarge)) graphregion(fcolor(white)) ylabel(,labsize(medsmall)) xlabel(, nogrid labsize(small)) legend(off) coeflabels(c1 = "Network TV News" c2 = "Cable TV News" c3 = "Local TV News" c4 = "Print newspapers" c5 = "Radio" c6 = "Online newspapers" c7 = "Online news magazines" c8 = "Blogs" c9 = "YouTube" c10 = "Facebook" c11 = "Twitter" c12 = "Reddit" c13 = "Instagram" c14 = "TV News websites" c15 = "8Kun" c16 = "Telegram" c17 = "Truth Social") 

asdoc pwcorr crypto OFFLINE_network OFFLINE_cable OFFLINE_local OFFLINE_print OFFLINE_radio, sig star(.05)

asdoc pwcorr crypto ONLINE_newspapers ONLINE_newsmags ONLINE_blogs ONLINE_YouTube ONLINE_Facebook ONLINE_Twitter ONLINE_Reddit ONLINE_Instagram ONLINE_TVnewssites ONLINE_8kun ONLINE_Telegram ONLINE_TruthSocial, sig star(.05)


							**Additional figures for Appendix**

**Horizontal bar graph of correlations between crypto and various CTs

ci2 crypto ct_aliens, corr
ci2 crypto ct_jews, corr
ci2 crypto ct_vaccines, corr
ci2 crypto ct_GMOs, corr
ci2 crypto ct_falseflags, corr
ci2 crypto ct_nuclear, corr
ci2 crypto ct_gayagenda, corr
ci2 crypto ct_repub_climate, corr
ci2 crypto ct_Repub_obama, corr
ci2 crypto ct_Dem_Trump, corr
ci2 crypto ct_Dem_1percent, corr
ci2 crypto qanon_believer, corr
ci2 crypto qanon_deepstate, corr
ci2 crypto qanon_trafficking, corr
ci2 crypto covid_threat, corr
ci2 crypto covid_bioweapon, corr
ci2 crypto covid_vaccine, corr
ci2 crypto covid_China, corr
ci2 crypto vf_Bidenfraud, corr
ci2 crypto vf_rigged, corr
ci2 crypto vf_Repsteal, corr
ci2 crypto rus_Putin, corr
ci2 crypto rus_USpolicies, corr
ci2 crypto rus_USaid, corr

    
matrix drop _all

preserve

		*make a 1-row matrix w/ correlations
matrix A = (0.260,0.322,0.210,0.202,0.324,0.189,0.249,0.200,0.179,0.173,0.181,0.308,0.202,0.252,0.186,0.157,0.214,0.102,0.119,0.121,0.226)
		*make a 1-row matrix w/ lower CIs
matrix B = (0.218,0.282,0.168,0.159,0.284,0.147,0.208,0.158,0.136,0.130,0.138,0.268,0.159,0.210,0.143,0.114,0.172,0.058,0.075,0.078,0.184)
		*make a 1-row matrix w/ upper CIs
matrix C = (0.300,0.361,0.252,0.243,0.363,0.231,0.290,0.242,0.221,0.215,0.223,0.348,0.244,0.292,0.228,0.199,0.256,0.145,0.162,0.164,0.267)
		*combine the lower and upper CIs, whreby lower is r1 and upper is r2
matrix D = (B \ C)
		*make the plot
		
set scheme cleanplots

coefplot matrix(A), ci(D) recast(bar) barwidth(0.5) finten(60) citop ciopt(recast(rcap) lcolor(black)) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medium)) graphregion(fcolor(white)) ylabel(,labsize(small)) xlabel(, nogrid labsize(small)) legend(off) coeflabels(c1 = "Contact with aliens being hidden" c2 = "Holocaust exaggerated on purpose" c3 = "Vaccine dangers hidden by doctors" c4 = "GMO dangers hidden from public" c5 = "School shootings are false flags" c6 = "Nulcear power danger cover-up" c7 = "Secret gay agenda" c8 = "Climate change hoax" c9 = "Obama faked citizenship" c10 = "Trump is Russian agent" c11 = "1% secretly controls govt/economy" c12 = "QAnon believer" c13 = "Deep state within our government" c14 = "Elites child sex trafficking" c15 = "COVID exaggerated for political purposes" c16 = "Coronavirus is a bioweapon" c17 = "COVID forced dangers vaccines on public" c18 = "COVID created in Chinese lab" c19 = "Joe Biden elected via voter fraud" c20 = "US elections are rigged" c21 = "Republicans steal elections") 

restore


**As a forest plot
set scheme s2color

coefplot matrix(A), ci(D) sort(, descending) xtitle("Point-biserial correlation coefficients", size(medium)) graphregion(fcolor(white)) ylabel(,labsize(small)) xlabel(, nogrid labsize(small)) legend(off) coeflabels(c1 = "Contact with aliens being hidden" c2 = "Holocaust exaggerated on purpose" c3 = "Vaccine dangers hidden by doctors" c4 = "GMO dangers hidden from public" c5 = "School shootings are false flags" c6 = "Nulcear power danger cover-up" c7 = "Secret gay agenda" c8 = "Climate change hoax" c9 = "Obama faked citizenship" c10 = "Trump is Russian agent" c11 = "1% secretly controls govt/economy" c12 = "QAnon believer" c13 = "Deep state within our government" c14 = "Elites child sex trafficking" c15 = "COVID exaggerated for political purposes" c16 = "Coronavirus is a bioweapon" c17 = "COVID forced dangers vaccines on public" c18 = "COVID created in Chinese lab" c19 = "Joe Biden elected via voter fraud" c20 = "US elections are rigged" c21 = "Republicans steal elections")  



